# sobre la EH19
rm(list=ls())
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#install.packages("survey")
#install.packages("srvyr")
library(survey)
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
##
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
##
## dotchart
library(srvyr)
##
## Attaching package: 'srvyr'
## The following object is masked from 'package:stats':
##
## filter
#bases
load(url("https://github.com/AlvaroLimber/R_aru/raw/master/data/eh19.RData"))
Existen dos librerÃas
No es el mismo que STATA… [w=], lo mas cercano a Stata es el prefijo svy…
Parámetro del Total,
\[t_y=\sum_U{y_k}\]
El estimador,
\[\hat{t}_y=\sum_s \frac{y_k}{\pi_k}=\sum_s {y_k*\frac{1}{\pi_k}}=\sum_s {y_k*w_k}\]
En R…
#survey
sd1<-svydesign(ids = ~upm, strata=~estrato,weights =~factor ,data=eh19p)
svyhist(~aestudio,design = sd1)
hist(eh19p$aestudio)
svymean(~aestudio,design=sd1,na.rm=T)
## mean SE
## aestudio 8.1501 0.0722
t1<-svyby(~aestudio,~depto+area,design=sd1, svymean, na.rm=T,deff = T)
table(cv(t1)>0.10)
##
## FALSE
## 18
cv(t1)
## Chuquisaca.Urbana La Paz.Urbana Cochabamba.Urbana Oruro.Urbana
## 0.04112228 0.01359558 0.01917086 0.03222194
## PotosÃ.Urbana Tarija.Urbana Santa Cruz.Urbana Beni.Urbana
## 0.04172358 0.02540172 0.01862134 0.03162047
## Pando.Urbana Chuquisaca.Rural La Paz.Rural Cochabamba.Rural
## 0.03068992 0.07204062 0.03434233 0.03935627
## Oruro.Rural PotosÃ.Rural Tarija.Rural Santa Cruz.Rural
## 0.03638799 0.06865782 0.03523620 0.04566724
## Beni.Rural Pando.Rural
## 0.05773332 0.03930840
confint(t1)
## 2.5 % 97.5 %
## Chuquisaca.Urbana 8.376470 9.845096
## La Paz.Urbana 9.568880 10.092802
## Cochabamba.Urbana 9.050918 9.757635
## Oruro.Urbana 9.146942 10.380153
## PotosÃ.Urbana 7.514075 8.852477
## Tarija.Urbana 8.377137 9.254977
## Santa Cruz.Urbana 8.646720 9.301789
## Beni.Urbana 7.669275 8.682688
## Pando.Urbana 8.362137 9.432504
## Chuquisaca.Rural 4.258291 5.658515
## La Paz.Rural 6.043890 6.916233
## Cochabamba.Rural 4.815068 5.619996
## Oruro.Rural 5.861075 6.761292
## PotosÃ.Rural 3.964398 5.197253
## Tarija.Rural 5.244833 6.023009
## Santa Cruz.Rural 5.044238 6.035987
## Beni.Rural 5.473158 6.869832
## Pando.Rural 5.940493 6.932248
summary(sd1)
## Stratified 1 - level Cluster Sampling design (with replacement)
## With (1047) clusters.
## svydesign(ids = ~upm, strata = ~estrato, weights = ~factor, data = eh19p)
## Probabilities:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0004584 0.0029599 0.0042334 0.0051684 0.0060003 0.0418970
## Stratum Sizes:
## 11 12 13 14 21 22 23 24
## obs 4080 8769 10408 7491 2499 2554 2494 1310
## design.PSU 93 218 270 213 65 69 79 40
## actual.PSU 93 218 270 213 65 69 79 40
## Data variables:
## [1] "folio" "depto" "area" "nro"
## [5] "s02a_02" "s02a_03" "s02a_04a" "s02a_04b"
## [9] "s02a_04c" "s02a_05" "s02a_06a" "s02a_06b"
## [13] "s02a_06c" "s02a_06d" "s02a_06e" "s02a_06_b"
## [17] "s02a_07_1" "s02a_07_2" "s02a_07_3" "s02a_08"
## [21] "s02a_10" "s03a_01a" "s03a_01b" "s03a_01c"
## [25] "s03a_01d" "s03a_01d2_cod" "s03a_01e" "s03a_02"
## [29] "s03a_02e" "s03a_03" "s03a_03a" "s03a_04"
## [33] "s03a_04npioc" "s04a_01a" "s04a_01b" "s04a_01e"
## [37] "s04a_02a" "s04a_02b" "s04a_02e" "s04a_03a"
## [41] "s04a_03b" "s04a_03c" "s04a_03d" "s04a_03e"
## [45] "s04a_03f" "s04a_03g" "s04a_04a" "s04a_04b"
## [49] "s04a_04e" "S04A_0" "S04A_1" "S04A_2"
## [53] "s04a_05a" "s04a_05b" "s04a_05c" "s04a_05d"
## [57] "s04a_05e" "s04a_06a" "s04a_07a" "s04a_07a_e"
## [61] "s04a_06b" "s04a_07b" "s04a_07b_e" "s04a_06c"
## [65] "s04a_07c" "s04a_07c_e" "s04a_06d" "s04a_07d"
## [69] "s04a_07d_e" "s04a_06e" "s04a_07e" "s04a_07e_e"
## [73] "s04a_06f" "s04a_07f" "s04a_07f_e" "s04a_06g"
## [77] "s04a_07g" "s04a_07g_e" "s04a_08" "s04a_08a1"
## [81] "s04a_08a2" "s04a_08b" "s04a_09" "s04a_09a"
## [85] "s04b_11a" "s04b_11b" "s04b_12" "s04b_13"
## [89] "s04b_14a" "s04b_14b" "s04b_15" "s04b_15e"
## [93] "S04B_9" "S04B_A" "S04B_B" "s04b_16"
## [97] "s04b_16e" "S04B_6" "S04B_7" "S04B_8"
## [101] "s04b_17" "s04b_17e" "S04B_3" "S04B_4"
## [105] "S04B_5" "s04b_18" "s04b_18e" "S04B_0"
## [109] "S04B_1" "S04B_2" "s04b_19" "s04b_20a1"
## [113] "s04b_20a2" "s04b_20b" "s04b_21a" "s04b_21b"
## [117] "s04b_21b2" "s04c_22" "s04c_23" "s04d_24"
## [121] "s04d_25" "s04d_26" "s04d_27a" "s04d_27b"
## [125] "s04e_28a" "s04e_28b" "s04e_29a" "s04e_29b"
## [129] "s04e_30a" "s04e_30b" "s04e_30c_cod" "s04e_31a"
## [133] "s04e_31b" "s04e_31c" "s04e_31d" "s04e_31e"
## [137] "s04e_31f" "s04e_31_e" "s04e_32a" "s04e_32b"
## [141] "s04e_33a" "s04e_33b" "s04_e_34a" "s04f_34"
## [145] "s04f_35a" "s04f_35b" "s04f_35c" "s04f_35e"
## [149] "s05a_01" "s05a_01a" "s05a_02a" "s05a_02c"
## [153] "s05a_03a" "s05a_03c" "s05a_04" "s05a_05"
## [157] "s05a_05_e" "s05a_06a" "s05a_06c" "s05a_07a"
## [161] "s05a_07b" "s05a_08" "s05a_09" "s05b_10"
## [165] "s05b_11" "s05b_11_e" "s05b_11a" "s05c_13a"
## [169] "s05c_13b" "s05c_13c" "s05c_13d" "s05c_13e"
## [173] "s05c_13f" "s05c_13g" "s05c_13h" "s05c_13_e"
## [177] "s05c_14a" "s05c_14b" "s05c_15a" "s05c_15b"
## [181] "s05d_17" "s05d_18" "s05d_19a" "s05d_19b"
## [185] "s05d_20a" "s05d_20b" "s05d_21a" "s05d_21b"
## [189] "s05d_21e" "s05d_22a" "s05d_22b" "s05d_22c"
## [193] "s05d_22d" "s05d_22e" "s05d_22f" "s05d_22g"
## [197] "s05d_22h" "s05d_22i" "s05d_22j" "s05d_22k"
## [201] "s05d_22l" "s05d_22_e" "s06a_01" "s06a_02"
## [205] "s06a_03" "s06a_04" "s06a_05" "s06a_06aa"
## [209] "s06a_06ab" "s06a_06ac" "s06a_06e" "s06a_07"
## [213] "s06a_08a" "s06a_08b" "s06a_09" "s06a_09e"
## [217] "s06a_10" "s06a_10e" "s06b_11a" "s06b_11a_cod"
## [221] "s06b_11b" "s06b_12a" "s06b_12a_cod" "s06b_12b"
## [225] "s06b_13" "s06b_13a" "s06b_13b" "s06b_13c"
## [229] "s06b_14" "s06b_15aa" "s06b_15ab" "s06b_15ba"
## [233] "s06b_15bb" "s06b_15ca" "s06b_15cb" "s06b_15da"
## [237] "s06b_15db" "s06b_17" "s06b_18" "s06b_19a"
## [241] "s06b_19b" "s06b_20" "s06b_20e" "s06b_21a"
## [245] "s06b_21b" "s06b_22" "s06b_23aa" "s06b_23ab"
## [249] "s06c_25a" "s06c_25b" "s06c_26a" "s06c_26b"
## [253] "s06c_27aa" "s06c_27ab" "s06c_27ba" "s06c_27bb"
## [257] "s06c_28a" "s06c_28a1" "s06c_28b" "s06c_29a"
## [261] "s06c_29b" "s06c_30a" "s06c_30a1" "s06c_30a2"
## [265] "s06c_30b" "s06c_30b1" "s06c_30b2" "s06c_30c"
## [269] "s06c_30c1" "s06c_30c2" "s06c_30d" "s06c_30d1"
## [273] "s06c_30d2" "s06c_30e" "s06c_30e1" "s06c_30e2"
## [277] "s06d_31a" "s06d_31b" "s06d_32aa" "s06d_32ab"
## [281] "s06d_32ba" "s06d_32bb" "s06d_32ca" "s06d_32cb"
## [285] "s06d_32da" "s06d_32db" "s06d_32ea" "s06d_32eb"
## [289] "s06d_32fa" "s06d_32fb" "s06d_32ga" "s06d_32gb"
## [293] "s06d_32ha" "s06d_32hb" "s06d_33a" "s06d_33b"
## [297] "s06d_34" "s06e_35a" "s06e_35a_cod" "s06e_35b"
## [301] "s06e_36" "s06e_37" "s06e_38a" "s06e_38b"
## [305] "s06e_39" "s06e_40" "s06e_40b" "s06f_42a"
## [309] "s06f_42b" "s06f_43a" "s06f_43a1" "s06f_43b"
## [313] "s06f_43b1" "s06f_43c" "s06f_43c1" "s06f_44a"
## [317] "s06f_44b" "s06f_45aa" "s06f_45ab" "s06f_45ba"
## [321] "s06f_45bb" "s06f_45ca" "s06f_45cb" "s06f_45da"
## [325] "s06f_45db" "s06f_45ea" "s06f_45eb" "s06f_45fa"
## [329] "s06f_45fb" "s06f_45ga" "s06f_45gb" "s06f_45ha"
## [333] "s06f_45hb" "s06f_46a" "s06f_46b" "s06g_47"
## [337] "s06g_48" "s06g_49" "s06g_49e" "s06g_50"
## [341] "s06g_50e" "s06g_51" "s06g_51e" "s06g_52"
## [345] "s06g_53" "s06g_54" "s06g_55" "s07a_01a"
## [349] "s07a_01b" "s07a_01c" "s07a_01d" "s07a_01e"
## [353] "s07a_01e0" "s07a_01e1" "s07a_01e1e" "s07a_01e2"
## [357] "s07a_01e2e" "s07a_02a" "s07a_02b" "s07a_02c"
## [361] "s07a_02ce" "s07a_03a" "s07a_03b" "s07a_03c"
## [365] "s07a_04a" "s07a_04b" "s07a_04c" "s07a_04d"
## [369] "s07b_05aa" "s07b_05ab" "s07b_05ba" "s07b_05bb"
## [373] "s07b_05ca" "s07b_05cb" "s07b_05da" "s07b_05db"
## [377] "s07b_05de" "s07b_05ea" "s07b_05eb" "s07b_05ee"
## [381] "s07c_06" "s07c_07" "s07c_08a" "s07c_08b"
## [385] "s07c_08e" "s07c_09" "s07c_09e" "s07c_10"
## [389] "s08a_01" "s08a_03a" "s08a_03b" "s08a_03c"
## [393] "s08a_03e" "s08a_04" "s08a_06" "upm"
## [397] "estrato" "factor" "tipohogar" "cobersalud"
## [401] "hnv_ult_a" "quienatenparto" "dondeatenparto" "niv_ed"
## [405] "niv_ed_g" "cmasi" "educ_prev" "aestudio"
## [409] "cob_op" "caeb_op" "pet" "ocupado"
## [413] "cesante" "aspirante" "desocupado" "pea"
## [417] "temporal" "permanente" "pei" "condact"
## [421] "phrs" "shrs" "tothrs" "yprilab"
## [425] "yseclab" "ylab" "ynolab" "yper"
## [429] "yhog" "yhogpc" "z" "zext"
## [433] "p0" "p1" "p2" "pext0"
## [437] "pext1" "pext2"
hist(eh19p$factor)
summary(eh19p$factor)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 23.87 166.66 236.21 291.21 337.84 2181.61
quantile(eh19p$factor,c(0.05,0.1,0.9,0.95))
## 5% 10% 90% 95%
## 78.91777 110.66443 514.98672 663.16821
#srvyr
sd2<-as_survey_design(sd1)
sd3<-eh19p %>% as_survey_design(ids=upm, strata=estrato, weights=factor)
sd2 %>% summarise(m_aes=survey_mean(aestudio,na.rm=T))
## # A tibble: 1 x 2
## m_aes m_aes_se
## <dbl> <dbl>
## 1 8.15 0.0722
sd3 %>% summarise(m_aes=survey_mean(aestudio,na.rm=T))
## # A tibble: 1 x 2
## m_aes m_aes_se
## <dbl> <dbl>
## 1 8.15 0.0722
sd3 %>% filter(s02a_03>=15) %>% summarise(m_aes=survey_mean(aestudio,na.rm=T))
## # A tibble: 1 x 2
## m_aes m_aes_se
## <dbl> <dbl>
## 1 9.92 0.0800
t2<-sd3 %>% filter(s02a_03>=15) %>% group_by(depto,area) %>% summarise(m_aes=survey_mean(aestudio,na.rm=T,deff=T,vartype=c("ci","cv","se")))
## Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## Please use the `.add` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
library(knitr)
kable(t2)
| depto | area | m_aes | m_aes_low | m_aes_upp | m_aes_cv | m_aes_se | m_aes_deff |
|---|---|---|---|---|---|---|---|
| Chuquisaca | Urbana | 10.895703 | 10.148803 | 11.642604 | 0.0349344 | 0.3806348 | 7.162531 |
| Chuquisaca | Rural | 5.693202 | 4.608445 | 6.777959 | 0.0971005 | 0.5528128 | 11.249735 |
| La Paz | Urbana | 11.595812 | 11.346048 | 11.845576 | 0.0109768 | 0.1272846 | 5.500503 |
| La Paz | Rural | 7.761397 | 7.192433 | 8.330360 | 0.0373585 | 0.2899545 | 2.984142 |
| Cochabamba | Urbana | 11.218832 | 10.845887 | 11.591777 | 0.0169412 | 0.1900601 | 7.237421 |
| Cochabamba | Rural | 6.114481 | 5.454174 | 6.774787 | 0.0550341 | 0.3365048 | 4.769871 |
| Oruro | Urbana | 11.637908 | 11.009817 | 12.265998 | 0.0275038 | 0.3200868 | 6.434192 |
| Oruro | Rural | 7.429932 | 6.775212 | 8.084653 | 0.0449073 | 0.3336581 | 3.888534 |
| Potosà | Urbana | 10.484944 | 9.787499 | 11.182389 | 0.0338992 | 0.3554312 | 4.338572 |
| Potosà | Rural | 5.216560 | 4.368462 | 6.064659 | 0.0828529 | 0.4322073 | 6.059638 |
| Tarija | Urbana | 10.821501 | 10.317403 | 11.325600 | 0.0237396 | 0.2568983 | 4.577131 |
| Tarija | Rural | 6.882555 | 6.059210 | 7.705900 | 0.0609646 | 0.4195923 | 5.004415 |
| Santa Cruz | Urbana | 11.053740 | 10.740448 | 11.367032 | 0.0144440 | 0.1596597 | 5.091124 |
| Santa Cruz | Rural | 6.695770 | 6.124629 | 7.266911 | 0.0434699 | 0.2910645 | 2.997842 |
| Beni | Urbana | 10.599824 | 10.070925 | 11.128722 | 0.0254284 | 0.2695368 | 4.254653 |
| Beni | Rural | 8.195720 | 7.419166 | 8.972275 | 0.0482870 | 0.3957469 | 3.680029 |
| Pando | Urbana | 11.428409 | 10.934781 | 11.922036 | 0.0220120 | 0.2515621 | 2.502778 |
| Pando | Rural | 8.783820 | 8.247287 | 9.320353 | 0.0311286 | 0.2734276 | 2.258157 |
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplot(eh19p,aes(ylab))+geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 23816 rows containing non-finite values (stat_bin).
ggplot(eh19p,aes(ylab))+geom_boxplot()
## Warning: Removed 23816 rows containing non-finite values (stat_boxplot).
ggplot(eh19p,aes(aestudio))+geom_bar()
## Warning: Removed 2903 rows containing non-finite values (stat_count).
ggplot(eh19p,aes(aestudio,weights=factor))+geom_bar()
## Warning: Removed 2903 rows containing non-finite values (stat_count).
ggplot(eh19p %>% filter(s02a_03>=15) ,aes(aestudio,weights=factor))+geom_bar()
## Warning: Removed 30 rows containing non-finite values (stat_count).
g1<-ggplot(eh19p %>% filter(s02a_03>=15) ,aes(aestudio,weights=factor))+geom_bar()
ggplotly(g1)
## Warning: Removed 30 rows containing non-finite values (stat_count).